According to U.S. Department of energy, buildings in the United States are responsible for about 39% of CO2 emissions and 73% of electricity consumption. This electrical usage is roughly divided equally between residential and commercial buildings. Depending on the specific use modality of the building, the dominant electricity consumers can be lighting and HVAC. Specifically for office buildings, data from the U.S. Energy Information Administration shows that lighting accounts for 39% and HVAC for 23% of electricity use. As such, any savings in lighting and HVAC in buildings, can result in substantial overall energy consumption in the United States.
Occupancy detection in current buildings is typically accomplished using passive infrared (PIR), Carbon Dioxide, and ultrasonic motion sensors installed expressly to determine occupancy and control building systems to reduce energy. Drawbacks to such explicit occupancy sensing include the cost of installing and maintaining sensors, limited accuracy, and lack of networking capabilities for data fusion and collection. One way to circumvent the cost associated with installing and maintaining occupancy sensors in buildings is to leverage existing WiFi infrastructure in commercial buildings. Existing WiFi based occupancy systems are experimental in nature, and generally exploit coarse grained information such as Authentication, Authorization and Accounting (AAA) logs of WiFi clients, and metadata information such WiFi MAC address and AP location within the building. This approach assumes prior knowledge of access points, and typically results in spatially coarse localization of the occupants. A lower cost and more accurate alternative to occupancy coupled with real-time climate control systems would be desirable.
As such, indoor climate control based on real-time occupancy detection utilizing multimodal sensor positioning is presented herein.